{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<h1> 1. Exploring natality dataset </h1>\n",
    "\n",
    "This notebook illustrates:\n",
    "<ol>\n",
    "<li> Exploring a BigQuery dataset using AI Platform Notebooks.\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# change these to try this notebook out\n",
    "BUCKET = 'cloud-training-demos-ml'\n",
    "PROJECT = 'cloud-training-demos'\n",
    "REGION = 'us-central1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['BUCKET'] = BUCKET\n",
    "os.environ['PROJECT'] = PROJECT\n",
    "os.environ['REGION'] = REGION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "if ! gcloud storage ls | grep -q gs://${BUCKET}/; then\n",    "  gcloud storage buckets create --location ${REGION} gs://${BUCKET}\n",    "fi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<h2> Explore data </h2>\n",
    "\n",
    "The data is natality data (record of births in the US). My goal is to predict the baby's weight given a number of factors about the pregnancy and the baby's mother.  Later, we will want to split the data into training and eval datasets. The hash of the year-month will be used for that -- this way, twins born on the same day won't end up in different cuts of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Create SQL query using natality data after the year 2000\n",
    "query = \"\"\"\n",
    "SELECT\n",
    "  weight_pounds,\n",
    "  is_male,\n",
    "  mother_age,\n",
    "  plurality,\n",
    "  gestation_weeks,\n",
    "  FARM_FINGERPRINT(CONCAT(CAST(YEAR AS STRING), CAST(month AS STRING))) AS hashmonth\n",
    "FROM\n",
    "  publicdata.samples.natality\n",
    "WHERE year > 2000\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight_pounds</th>\n",
       "      <th>is_male</th>\n",
       "      <th>mother_age</th>\n",
       "      <th>plurality</th>\n",
       "      <th>gestation_weeks</th>\n",
       "      <th>hashmonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.063611</td>\n",
       "      <td>True</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7108882242435606404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.687028</td>\n",
       "      <td>True</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7170969733900686954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.561856</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6392072535155213407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.561856</td>\n",
       "      <td>True</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2126480030009879160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.312733</td>\n",
       "      <td>True</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3408502330831153141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight_pounds  is_male  mother_age  plurality  gestation_weeks  \\\n",
       "0       7.063611     True          32          1             37.0   \n",
       "1       4.687028     True          30          3             33.0   \n",
       "2       7.561856     True          20          1             39.0   \n",
       "3       7.561856     True          31          1             37.0   \n",
       "4       7.312733     True          32          1             40.0   \n",
       "\n",
       "             hashmonth  \n",
       "0  7108882242435606404  \n",
       "1  7170969733900686954  \n",
       "2  6392072535155213407  \n",
       "3  2126480030009879160  \n",
       "4  3408502330831153141  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call BigQuery and examine in dataframe\n",
    "from google.cloud import bigquery\n",
    "df = bigquery.Client().query(query + \" LIMIT 100\").to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's write a query to find the unique values for each of the columns and the count of those values.\n",
    "This is important to ensure that we have enough examples of each data value, and to verify our hunch that the parameter has predictive value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Create function that finds the number of records and the average weight for each value of the chosen column\n",
    "def get_distinct_values(column_name):\n",
    "  sql = \"\"\"\n",
    "SELECT\n",
    "  {0},\n",
    "  COUNT(1) AS num_babies,\n",
    "  AVG(weight_pounds) AS avg_wt\n",
    "FROM\n",
    "  publicdata.samples.natality\n",
    "WHERE\n",
    "  year > 2000\n",
    "GROUP BY\n",
    "  {0}\n",
    "  \"\"\".format(column_name)\n",
    "  return bigquery.Client().query(sql).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5haokNZDhLkkNZLhLUgMZ7pLUQIa7JDWQ4S5JDWS4S1IDGe6S1ECGuyQ1kOEuSQ1kuEtSAxnuktRAhrskNZDhLkkNZLhLUgO1Fe5J5ie5J8mqJGeOUPfuJJWkp3MtSpI21qjhnmQycAFwJLAXsCDJXsPU7Qj8DfDTTjcpSdo47Ry5Hwisqqp7q+o54Arg2GHq/h74HPBMB/uTJG2CdsJ9F2D1oPm+1rIXJNkf2LWqvj3ShpIsTNKbpLe/v3+jm5UktWfMH6gmmQR8EfjYaLVVtaiqeqqqZ8aMGWPdtSRpA9oJ9weAXQfNz2wtW29HYG/gR0l+BbwJWOyHqpLUPe2E+y3AnCSzk2wLHAcsXr+yqh6rqulVNauqZgE3A8dUVe9m6ViSNKpRw72q1gKnAtcDK4CrquruJOcmOWZzNyhJ2njbtFNUVUuAJUOWnb2B2nljb0uSNBZeoSpJDWS4S1IDGe6S1ECGuyQ1kOEuSQ1kuEtSAxnuktRAhrskNZDhLkkNZLhLUgMZ7pLUQIa7JDWQ4S5JDWS4S1IDGe6S1EBthXuS+UnuSbIqyZnDrP9okp8nuTPJ95Ps3vlWJUntGjXck0wGLgCOBPYCFiTZa0jZ7UBPVe0DfBP4fKcblSS1r50j9wOBVVV1b1U9B1wBHDu4oKp+WFVPt2ZvZuAh2pKkLmkn3HcBVg+a72st25CTge8MtyLJwiS9SXr7+/vb71KStFE6+oFqkhOAHuC84dZX1aKq6qmqnhkzZnRy15KkQdp5QPYDwK6D5me2lr1EkrcCfwe8paqe7Ux7kqRN0c6R+y3AnCSzk2wLHAcsHlyQZC7wP4BjqurBzrcpSdoYo4Z7Va0FTgWuB1YAV1XV3UnOTXJMq+w8YAfg6iTLkyzewOYkSVtAO8MyVNUSYMmQZWcPmn5rh/uSJI2BV6hKUgMZ7pLUQIa7JDWQ4S5JDWS4S1IDGe6S1ECGuyQ1kOEuSQ1kuEtSAxnuktRAhrskNZDhLkkNZLhLUgMZ7pLUQIa7JDWQ4S5JDdRWuCeZn+SeJKuSnDnM+u2SXNla/9MkszrdqCSpfaOGe5LJwAXAkcBewIIkew0pOxl4pKr+CPgH4HOdblSS1L52jtwPBFZV1b1V9RxwBXDskJpjgX9qTX8TODxJOtemJGljtPMM1V2A1YPm+4A3bqimqtYmeQzYGfjd4KIkC4GFrdknk9yzKU1rWNMZ8vPeGsX3dBORv5udtXs7RW09ILtTqmoRsGhL7nOiSNJbVT3d7kMayt/N7mhnWOYBYNdB8zNby4atSbINMA14qBMNSpI2XjvhfgswJ8nsJNsCxwGLh9QsBk5sTb8H+EFVVefalCRtjFGHZVpj6KcC1wOTgYur6u4k5wK9VbUY+J/AZUlWAQ8z8AdAW5bDXdpa+bvZBfEAW5KaxytUJamBDHdJaiDDXdJmkWS7bvcwkRnukjoqyYFJfgasbM3vm+TLXW5rwjHcx7EMOCHJ2a353ZIc2O2+NOF9CTia1rUuVXUHcFhXO5qADPfx7ULgIGBBa/4JBm7yJnXTpKr69ZBlz3elkwlsi95+QB33xqraP8ntAFX1SOtCM6mbVrfeQVbrrrKnAb/ock8Tjkfu49ua1j+eAkgyA1jX3ZYkPgJ8FNgN+C3wptYybUFexDSOJTkeeC+wPwO3XH4P8KmqurqrjUnqOsN9nEuyB3A4EOD7VbWiyy1pgkvyNVrvJgerqoXDlGszccx9HEvyWuC+qrogyTzgiCS/qapHu9yaJrbvDZqeCryTlz4TQluAR+7jWJLlQA8wC/g2A3fnfH1VvaObfUmDJZkE3FhVB3e7l4nED1THt3VVtRZ4F3B+VZ0OvLrLPUlDzQb+oNtNTDQOy4xva5IsAD4A/KfWsild7EciySO8OOY+iYHbgJ/ZvY4mJsN9fDsJ+Cvgs1V1X5LZwGVd7kkTWJIA+/Li09rW+eCe7nDMXVJHJbmrqvbudh8TnUfu41Drpkwb/KtcVftswXakoZYnmVtVt3e7kYnMI/dxKMnuI60f5r4e0maXZJvWYznvBv4Y+CXwFAPXYFRV7d/VBicYw11SRyS5rXWvo9cOt76qfrmle5rIHJYZx5K8CfgysCewLQMPMH+qql7Z1cY0UQUM8a2F4T6+nQ8cB1zNwMVMHwBe19WONJHNSPLRDa2sqi9uyWYmOi9iGueqahUwuaqer6r/Bczvdk+asCYDOwA7buBLW5BH7uPb0637ty9P8nngN/gHW93zm6o6t9tNaIBBML69n4H/h6cycFbCrsC7u9qRJrJ0uwG9yLNlxqEku1XV/d3uQxosyauq6uFu96EBHrmPT9eun0hyTTcbkdYz2Lcuhvv4NPjt7x92rQtJWy3DfXyqDUxLEuCY+7iU5HlevKx7e+Dp9asYuMzbi5ikCc5wl6QGclhGkhrIcJekBjLcJamBDHc1SpKbtvD+ZiW5a0vuU2qH4a5GqaqDu92DtDUw3NUoSZ5s/ffVSZYmWZ7kriSHjPSaJOcluTvJ95IcmORHSe5NckyrZlaSHye5rfX1sj8iSSa3tnNLkjuTnLL5vlNpZIa7mup9wPVVtR+wL7B8hNpXAD+oqtcDTwCfAY4A3gmsv8vhg8ARrUfFvRf40jDbORl4rKoOAA4APpxkdie+GWljectfNdUtwMVJpgDXVtVI4f4ccF1r+mfAs1W1pvUg8lmt5VOA85PsBzzP8A9FeRuwT5L3tOanAXOA+8b0nUibwHBXI1XV0iSHAkcBlyT5YlVduoHyNfXi1XzrgGdb21iXZP2/kf8C/JaBdwGTgGeG2U6A06rq+k59H9KmclhGjZRkd+C3VfU14CJg/zFuchoDD6NYx8B99CcPU3M98JHWuwWSvC7JK8a4X2mTeOSuppoHnJ5kDfAkA8+XHYsLgWuSfICBIZynhqm5iIFhnNuSBOgH/myM+5U2ifeWkaQGclhGkhrIYRlNGEl+Cmw3ZPH7q+pn3ehH2pwclpGkBnJYRpIayHCXpAYy3CWpgQx3SWqg/w9YnRB5/p/vXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot to see is_male with avg_wt linear and num_babies logarithmic\n",
    "df = get_distinct_values('is_male')\n",
    "df.plot(x='is_male', y='num_babies', kind='bar');\n",
    "df.plot(x='is_male', y='avg_wt', kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line plots to see mother_age with avg_wt linear and num_babies logarithmic\n",
    "df = get_distinct_values('mother_age')\n",
    "df = df.sort_values('mother_age')\n",
    "df.plot(x='mother_age', y='num_babies');\n",
    "df.plot(x='mother_age', y='avg_wt');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot to see plurality(singleton, twins, etc.) with avg_wt linear and num_babies logarithmic\n",
    "df = get_distinct_values('plurality')\n",
    "df = df.sort_values('plurality')\n",
    "df.plot(x='plurality', y='num_babies', logy=True, kind='bar');\n",
    "df.plot(x='plurality', y='avg_wt', kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot to see gestation_weeks with avg_wt linear and num_babies logarithmic\n",
    "df = get_distinct_values('gestation_weeks')\n",
    "df = df.sort_values('gestation_weeks')\n",
    "df.plot(x='gestation_weeks', y='num_babies', logy=True, kind='bar');\n",
    "df.plot(x='gestation_weeks', y='avg_wt', kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "All these factors seem to play a part in the baby's weight. Male babies are heavier on average than female babies. Teenaged and older moms tend to have lower-weight babies. Twins, triplets, etc. are lower weight than single births. Preemies weigh in lower as do babies born to single moms. In addition, it is important to check whether you have enough data (number of babies) for each input value. Otherwise, the model prediction against input values that doesn't have enough data may not be reliable.\n",
    "<p>\n",
    "In the next notebook, I will develop a machine learning model to combine all of these factors to come up with a prediction of a baby's weight."
   ]
  },
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    "Copyright 2017-2018 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License"
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